Conserved IFN Signature between Adult and Pediatric Eosinophilic Esophagitis.
Melanie A RuffnerAlex HuJulianna DilolloKassidy BenocekDonna M ShowsMichael GluckJonathan M SpergelSteven F ZieglerDavid A HillKaren CerosalettiPublished in: Journal of immunology (Baltimore, Md. : 1950) (2021)
Eosinophilic esophagitis (EoE) is an allergic inflammatory disease of the esophagus that occurs in both children and adults. Previous studies of affected tissue from pediatric cohorts have identified prominent signatures of eosinophilia and type 2 inflammation. However, the details of the immune response in adults with EoE are still being elucidated. To determine whether EoE in adults shares inflammatory profiles with those observed in children, we performed RNA sequencing of paired human esophageal biopsies and blood samples from adults with EoE or gastroesophageal reflux disease. Unbiased analysis of differentially expressed genes in tissue revealed a strong IFN signature that was significantly enriched in EoE patients as compared with patients with gastroesophageal reflux disease. Both type I and type II IFN-responsive genes were upregulated in adult biopsies, but not in blood. A similar increase in expression of IFN gene sets was observed in pediatric EoE biopsies as compared with non-EoE samples, and in public pediatric and adult RNA-sequencing data. Finally, we found that human peripheral CD4+ T cells from children with EoE produce IFN-γ upon activation with EoE-causal allergens. Together, this work identifies a conserved IFN signature in pediatric and adult EoE, highlighting a role for non-type 2 inflammatory networks in the disease process in humans.
Keyphrases
- immune response
- dendritic cells
- genome wide
- gastroesophageal reflux disease
- childhood cancer
- oxidative stress
- young adults
- endothelial cells
- single cell
- end stage renal disease
- emergency department
- mental health
- prognostic factors
- ultrasound guided
- gene expression
- machine learning
- chronic kidney disease
- inflammatory response
- artificial intelligence
- deep learning
- cancer therapy
- peritoneal dialysis
- adverse drug
- electronic health record
- drug delivery